evostoc 2012 - 9th European event on evolutionary algorithms in stochastic and dynamic environments

Evolutionary Algorithms in Stochastic and Dynamic Environments
In many real-world optimisation problems, a wide range of uncertainties has to be taken into account. Generally, uncertainties in evolutionary optimisation can be categorized into four classes:

Noisy fitness function. Noise in fitness evaluations may come from many different sources such as sensory measurement errors or randomised simulations.
Approximated fitness function. When the fitness function is very expensive to evaluate, or an analytical fitness function is not available, approximated fitness functions are often used instead.
Robustness. Often, when a solution is implemented, the design variables or the environmental parameters are subject to perturbations or changes. Therefore, a common requirement is that a solution should still work satisfyingly either when the design variables change slightly, e.g., due to manufacturing tolerances, or when the environmental parameters vary slightly. This issue is generally known as the search for robust solutions.
Dynamic fitness function. In a changing environment, it should be possible to continuously track the moving optimum rather than repeatedly re-start the optimisation process. For evolutionary computation in dynamic environments, learning and adaptation usually play an important role. Multi-objective problems may also involve dynamic environments.
Handling uncertainties in evolutionary computation has received an increasing interest over the past years. A variety of methods for addressing uncertainties have been reported from different application backgrounds. The evostoc event’s objective is to foster interest in the issue of handling uncertainties, to provide a forum for researchers to meet, and a platform to present and discuss latest research in the field. Papers are solicited addressing any of the aforementioned four areas and/or their combination with optimisation methods inspired by nature. Algorithmic solutions for multi-objective/multi-criteria problems and novel implementation of hybrid (memetic) algorithms are warmly encouraged. Theoretical and empirical results as well as real-world applications are welcome.

Areas of Interest and Contributions
Topics of interest include but are not limited to the following:

handling noisy fitness functions
using fitness approximations
searching for robust solutions
tracking moving optima
multi-objective problems in uncertain environments
co-evolution in uncertain environments
real-world applications